Collecting information on node properties in a network and generating statistical analysis is useful in many aspects. The goal is to generate useful statistical information on nodes' properties. An example would be a network in which the nodes are customers and the gathered information is used to plan a marketing campaign. Example of systems using this information is churn prediction and up-sell lead generation systems.
One goal of service providers is to maximize their customer base, and consequently maximize the profit earned from their customers. Thus, customer acquisition and retention are important aspects of the operation of a service provider. One primary vehicle for acquiring customers is to utilize lists of potential customers, known as leads. The current state of the art fails to utilize the wealth of information the service provider has to generate potential leads. As such, leads are often simply lists of potential customers without any clearly defined relation to the service provider. Since these leads are not generated using the underlying network data, many of the leads will have a low probability for successful acquisition.
Another problem complicating the generation of leads is that while service providers have a wealth of information regarding their own customers' usages and habits, they know significantly less about people or entities that are not their customers or which are the customers of rival service providers. For example, a telecom provider knows the name, billing information, account type, etc., of a given customer in a telecommunications network. In contrast, the information a service provider has about others who aren't customers is often limited to the interactions between the rival provider's customers and their own customers.
Thus there exists a need in the current state of the art for systems and methods for identifying high quality leads in a network containing both known nodes of a service provider and unknown nodes belonging to rival service providers.